Final Project

August 11 2024
Preapared by Yen-Ming (Max) Chen

Introduction

Everyone loves trees; they are essential for our environment and planet, and they are nice to look at. To gain a better understanding of the local distribution and statistics of trees around the greater Vancouver area, we will be conducting an analysis and formulate a report using a subset of "Vancouver Street Trees" dataset. The dataset used is provided by the City of Vancouver, who compiled and rightfully owns it, and was shortened and cleaned up (to an extent) by the faculties of UBC's "Intro to Data Visualization" course.

The report aims to answer the following questions that center around the diameter, height range, and genus of trees, as well as them being on/off the curb, in all neighborhoods.

Questions of Interest

1. Which neighborhood has the most amount of trees planted? And how many of those trees were planted on/off the curb?
2. What is the distribution, including quartile, median, min/max, and outliers, of the height range and diameter in each neighborhood?
3. What are the numbers of trees of all genera in each neighborhood?
4. What are the diameter and height range for all trees in all neighbourhoods and of all genera?

As we begin our analysis later on, we will be focusing on dimension-related columns, such as height, diameter, and count, as well as genus and neighborhood names. The report will start off with more of a macro view of all trees and neighborhoods before focusing more on the distributions of different categories across all neighborhoods. Once all plots have been assembled, we will put together a dashboard of all graphs at the end of the report!

Analysis

Data & Library Imports

# Necessary libraries
import altair as alt
import pandas as pd

# alt.data_transformers.enable('data_server') 

# "Vancouver Street Trees" dataset import
trees_df = pd.read_csv('https://raw.githubusercontent.com/UBC-MDS/data_viz_wrangled/main/data/Trees_data_sets/small_unique_vancouver.csv')
trees_df

	Unnamed: 0	std_street	on_street	species_name	neighbourhood_name	date_planted	diameter	street_side_name	genus_name	assigned	...	plant_area	curb	tree_id	common_name	height_range_id	on_street_block	cultivar_name	root_barrier	latitude	longitude
0	10747	W 20TH AV	W 20TH AV	PLATANOIDES	Riley Park	2000-02-23	28.5	EVEN	ACER	N	...	15	Y	21421	NORWAY MAPLE	4	0	NaN	N	49.252711	-123.106323
1	12573	W 18TH AV	W 18TH AV	CALLERYANA	Arbutus-Ridge	1992-02-04	6.0	ODD	PYRUS	N	...	7	Y	129645	CHANTICLEER PEAR	2	2300	CHANTICLEER	N	49.256350	-123.158709
2	29676	ROSS ST	ROSS ST	NIGRA	Sunset	NaN	12.0	ODD	PINUS	N	...	7	Y	154675	AUSTRIAN PINE	4	7800	NaN	N	49.213486	-123.083254
3	8856	DOMAN ST	DOMAN ST	AMERICANA	Killarney	1999-11-12	11.0	EVEN	FRAXINUS	N	...	7	Y	180803	AUTUMN APPLAUSE ASH	4	6900	AUTUMN APPLAUSE	N	49.220839	-123.036721
4	21098	EAST BOULEVARD	EAST BOULEVARD	HIPPOCASTANUM	Shaughnessy	NaN	15.5	ODD	AESCULUS	Y	...	N	Y	74364	COMMON HORSECHESTNUT	4	5200	NaN	N	49.238514	-123.154958
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
4995	6132	E 53RD AV	E 53RD AV	SERRULATA	Victoria-Fraserview	NaN	17.0	EVEN	PRUNUS	N	...	9	Y	47059	KWANZAN FLOWERING CHERRY	2	2200	KWANZAN	N	49.221161	-123.061023
4996	5642	E 32ND AV	E 32ND AV	XX	Kensington-Cedar Cottage	2014-01-14	3.0	EVEN	CORNUS	N	...	10	N	247874	EDDIES WHITE WONDER DOGWOOD	1	1700	EDDIE'S WHITE WONDER	N	49.241544	-123.070644
4997	8777	DAWSON ST	DAWSON ST	TULIPIFERA	Killarney	2002-04-15	3.5	EVEN	LIRIODENDRON	N	...	7	Y	192642	ARNOLD TULIPTREE	2	6500	ARNOLD	N	49.224511	-123.048723
4998	23489	E 13TH AV	E 13TH AV	INVOLUCRATA	Mount Pleasant	2003-12-02	5.5	EVEN	DAVIDIA	N	...	5	Y	202500	DOVE OR HANDKERCHIEF TREE	1	300	NaN	Y	49.259208	-123.096905
4999	7450	CULLODEN ST	CULLODEN ST	CAMPESTRE	Kensington-Cedar Cottage	NaN	3.0	ODD	ACER	N	...	8	Y	259433	RED SHINE MAPLE	1	4500	RED SHINE	N	49.243772	-123.078967

5000 rows × 21 columns

Dataset Description

trees_df.info()
print("\n")
trees_df.describe()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5000 entries, 0 to 4999
Data columns (total 21 columns):
 #   Column              Non-Null Count  Dtype  
---  ------              --------------  -----  
 0   Unnamed: 0          5000 non-null   int64  
 1   std_street          5000 non-null   object 
 2   on_street           5000 non-null   object 
 3   species_name        5000 non-null   object 
 4   neighbourhood_name  5000 non-null   object 
 5   date_planted        2363 non-null   object 
 6   diameter            5000 non-null   float64
 7   street_side_name    5000 non-null   object 
 8   genus_name          5000 non-null   object 
 9   assigned            5000 non-null   object 
 10  civic_number        5000 non-null   int64  
 11  plant_area          4950 non-null   object 
 12  curb                5000 non-null   object 
 13  tree_id             5000 non-null   int64  
 14  common_name         5000 non-null   object 
 15  height_range_id     5000 non-null   int64  
 16  on_street_block     5000 non-null   int64  
 17  cultivar_name       2658 non-null   object 
 18  root_barrier        5000 non-null   object 
 19  latitude            5000 non-null   float64
 20  longitude           5000 non-null   float64
dtypes: float64(3), int64(5), object(13)
memory usage: 820.4+ KB

	Unnamed: 0	diameter	civic_number	tree_id	height_range_id	on_street_block	latitude	longitude
count	5000.000000	5000.000000	5000.000000	5000.000000	5000.00000	5000.000000	5000.000000	5000.000000
mean	14861.920400	12.340888	2975.707600	128682.584600	2.73440	2960.227000	49.247349	-123.107128
std	8680.023278	9.266600	2078.580429	75412.260406	1.56957	2086.861052	0.021251	0.049137
min	2.000000	0.000000	2.000000	36.000000	0.00000	0.000000	49.202783	-123.220560
25%	7192.750000	4.000000	1300.500000	61321.500000	2.00000	1300.000000	49.230152	-123.144178
50%	14870.000000	10.000000	2639.000000	130130.500000	2.00000	2600.000000	49.247981	-123.105861
75%	22366.750000	18.000000	4123.000000	191332.000000	4.00000	4100.000000	49.263275	-123.063484
max	29992.000000	71.000000	9113.000000	270750.000000	9.00000	9100.000000	49.293930	-123.023311

As evident in the output above, the "Vancouver Street Trees" (now known as trees_df) contains 21 columns and 5,000 rows. Within the 21 columns, three contain null values, such as 'date_planted', 'cultivar_name', and 'plant_area', while two others entail ambiguous meaning to their data entries, like 'Unnamed: 0' and 'assigned'; all will be filtered out and cleaned up in the upcoming data wrangling section.

Data Wrangling

trees_df = trees_df.drop(columns=['Unnamed: 0', 'assigned', 'date_planted', 'cultivar_name', 'plant_area'])
trees_df

	std_street	on_street	species_name	neighbourhood_name	diameter	street_side_name	genus_name	civic_number	curb	tree_id	common_name	height_range_id	on_street_block	root_barrier	latitude	longitude
0	W 20TH AV	W 20TH AV	PLATANOIDES	Riley Park	28.5	EVEN	ACER	66	Y	21421	NORWAY MAPLE	4	0	N	49.252711	-123.106323
1	W 18TH AV	W 18TH AV	CALLERYANA	Arbutus-Ridge	6.0	ODD	PYRUS	2323	Y	129645	CHANTICLEER PEAR	2	2300	N	49.256350	-123.158709
2	ROSS ST	ROSS ST	NIGRA	Sunset	12.0	ODD	PINUS	7855	Y	154675	AUSTRIAN PINE	4	7800	N	49.213486	-123.083254
3	DOMAN ST	DOMAN ST	AMERICANA	Killarney	11.0	EVEN	FRAXINUS	6938	Y	180803	AUTUMN APPLAUSE ASH	4	6900	N	49.220839	-123.036721
4	EAST BOULEVARD	EAST BOULEVARD	HIPPOCASTANUM	Shaughnessy	15.5	ODD	AESCULUS	5295	Y	74364	COMMON HORSECHESTNUT	4	5200	N	49.238514	-123.154958
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
4995	E 53RD AV	E 53RD AV	SERRULATA	Victoria-Fraserview	17.0	EVEN	PRUNUS	2248	Y	47059	KWANZAN FLOWERING CHERRY	2	2200	N	49.221161	-123.061023
4996	E 32ND AV	E 32ND AV	XX	Kensington-Cedar Cottage	3.0	EVEN	CORNUS	1708	N	247874	EDDIES WHITE WONDER DOGWOOD	1	1700	N	49.241544	-123.070644
4997	DAWSON ST	DAWSON ST	TULIPIFERA	Killarney	3.5	EVEN	LIRIODENDRON	6512	Y	192642	ARNOLD TULIPTREE	2	6500	N	49.224511	-123.048723
4998	E 13TH AV	E 13TH AV	INVOLUCRATA	Mount Pleasant	5.5	EVEN	DAVIDIA	360	Y	202500	DOVE OR HANDKERCHIEF TREE	1	300	Y	49.259208	-123.096905
4999	CULLODEN ST	CULLODEN ST	CAMPESTRE	Kensington-Cedar Cottage	3.0	ODD	ACER	4565	Y	259433	RED SHINE MAPLE	1	4500	N	49.243772	-123.078967

5000 rows × 16 columns

To facilitate ease of reading and save ourselves the trouble of renaming titles again and again, let's rename all columns to a reader-friendly format!

trees_df = trees_df.rename(columns={
    'std_street': 'Standard street',
    'on_street': 'On street',
    'species_name': 'Species',
    'neighbourhood_name': 'Neighbourhood',
    'diameter': 'Diameter',
    'street_side_name': 'Street side',
    'genus_name': 'Genus',
    'civic_number': 'Civic number',
    'curb': 'Curb',
    'tree_id': 'Tree ID',
    'common_name': 'Common name',
    'height_range_id': 'Height',
    'on_street_block': 'On street block',
    'root_barrier': 'Root barrier',
    'latitude': 'Latitude',
    'longitude': 'Longitude'
})

trees_df

	Standard street	On street	Species	Neighbourhood	Diameter	Street side	Genus	Civic number	Curb	Tree ID	Common name	Height	On street block	Root barrier	Latitude	Longitude
0	W 20TH AV	W 20TH AV	PLATANOIDES	Riley Park	28.5	EVEN	ACER	66	Y	21421	NORWAY MAPLE	4	0	N	49.252711	-123.106323
1	W 18TH AV	W 18TH AV	CALLERYANA	Arbutus-Ridge	6.0	ODD	PYRUS	2323	Y	129645	CHANTICLEER PEAR	2	2300	N	49.256350	-123.158709
2	ROSS ST	ROSS ST	NIGRA	Sunset	12.0	ODD	PINUS	7855	Y	154675	AUSTRIAN PINE	4	7800	N	49.213486	-123.083254
3	DOMAN ST	DOMAN ST	AMERICANA	Killarney	11.0	EVEN	FRAXINUS	6938	Y	180803	AUTUMN APPLAUSE ASH	4	6900	N	49.220839	-123.036721
4	EAST BOULEVARD	EAST BOULEVARD	HIPPOCASTANUM	Shaughnessy	15.5	ODD	AESCULUS	5295	Y	74364	COMMON HORSECHESTNUT	4	5200	N	49.238514	-123.154958
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
4995	E 53RD AV	E 53RD AV	SERRULATA	Victoria-Fraserview	17.0	EVEN	PRUNUS	2248	Y	47059	KWANZAN FLOWERING CHERRY	2	2200	N	49.221161	-123.061023
4996	E 32ND AV	E 32ND AV	XX	Kensington-Cedar Cottage	3.0	EVEN	CORNUS	1708	N	247874	EDDIES WHITE WONDER DOGWOOD	1	1700	N	49.241544	-123.070644
4997	DAWSON ST	DAWSON ST	TULIPIFERA	Killarney	3.5	EVEN	LIRIODENDRON	6512	Y	192642	ARNOLD TULIPTREE	2	6500	N	49.224511	-123.048723
4998	E 13TH AV	E 13TH AV	INVOLUCRATA	Mount Pleasant	5.5	EVEN	DAVIDIA	360	Y	202500	DOVE OR HANDKERCHIEF TREE	1	300	Y	49.259208	-123.096905
4999	CULLODEN ST	CULLODEN ST	CAMPESTRE	Kensington-Cedar Cottage	3.0	ODD	ACER	4565	Y	259433	RED SHINE MAPLE	1	4500	N	49.243772	-123.078967

5000 rows × 16 columns

As great as it would have been to include 'date_planted' in our analysis, due to the significant amount of null values present in that column, it would be best to exclude it instead. With the data set now wrangled, it is time to move onto the next part of our report — answering the previously proposed questions!

Question 1: Which neighborhood has the most amount of trees planted? And how many of those trees were planted on/off the curb?

A histogram chart would be the best fit to this question, as bar charts are easy to compare both against each bar and against other bar graphs. Figure 01 will comprise of stacked bars without any color differentiation for the 'Curb' category; this is done so to give the best overall look at the total numbers of trees in each neighbourhood.
Figure 02, on the other hand, will feature unstacked bars to more effectively communicate the difference of the numbers of trees planted on/off the curb in their respective neighbourhoods. Both plots will feature a 'clickable' selection option to provide more visual clarity for the readers/audeince. Additionally, tooltip will be added to indicate the exact count for each bar.

# Groupby to get the maximum count for the 'Neighbourhood' column
most_trees = trees_df.groupby('Neighbourhood').count().idxmax()

# Graph titles and subtitles
Neighbour_Fig01Titles = alt.TitleParams('Fig 01: Renfrew-Collingwood has the most amount of trees planted in TOTAL', 
                            subtitle='Histogram of trees planted on and off the curb combined in all neighbourhoods', 
                             anchor='middle')
Neighbour_Fig02Titles = alt.TitleParams('Fig 02: Dunbar-Southlands has the most amount of trees planted OFF the curb', 
                            subtitle='Renfrew-Collingwood has the most amount of trees planted ON the curb', 
                             anchor='middle')

# Selection code
Neighbour_Click = alt.selection_multi(fields=['Neighbourhood'])
Curb_Legend = alt.selection_multi(fields=['Curb'], bind='legend')

# Figure 01
Neighbour_StackTrue = alt.Chart(trees_df).mark_bar().encode(
    alt.X('count()', title='Amount of trees', stack=True), 
    alt.Y('Neighbourhood', sort='x'),
    opacity=alt.condition(Neighbour_Click, alt.value(0.8), alt.value(0.1)),
    tooltip=['count()']
    ).properties(title=Neighbour_Fig01Titles).add_selection(Neighbour_Click)

# Figure 02
Neighbour_StackFalse = alt.Chart(trees_df).mark_bar().encode(
    alt.X('count()', title='Amount of trees', stack=False), 
    alt.Y('Neighbourhood', sort='x'),
    alt.Color('Curb'),
    opacity=alt.condition(Neighbour_Click, alt.value(0.8), alt.value(0.1)),
    tooltip=['count()']
    ).properties(title=Neighbour_Fig02Titles).add_selection(Neighbour_Click)

# Two graphs put together
Neighbour_Histogram = alt.hconcat(Neighbour_StackTrue,Neighbour_StackFalse)
Neighbour_Histogram

Based on the output of both Figure 01 and 02, we can see that Renfrew-Collingwood not only has the title of "most trees planted on the curb" but also "most trees planted overall" when compared to all other neighbourhoods. Dunbar-Southland takes crown when examining only the trees planted off the curb.

Question 02: What is the distribution, including quartile, median, min/max, and outliers, of the height range and diameter in each neighborhood?

Since we wish to look more closely at the statistics and distribution of trees in all neighbourhoods, it is best to graph a boxplot. To answer Question 02, we will be putting 'Neighbourhood' on the Y-axis and 'Height range' and 'Diameter' on the X-axis to form a repeat plot. Additionally, we will also be adding tooltip for the X-axis columns to gain a better gauge on the specific value of each outliers!

# Boxplot title and subtitle
Boxplot_Titles = alt.TitleParams('Fig 03: The median value for height range is around 2 to 3 while for diameter is about 10', 
                                subtitle = 'Boxplot of height range and diameter in all neighbourhoods', 
                                anchor='middle')

# Figure 03
Neighbour_BoxPlot = alt.Chart(trees_df).mark_boxplot().encode(
    alt.X(alt.repeat('column'), type='quantitative'), 
    alt.Y('Neighbourhood:N'),
    tooltip=['Height', 'Diameter']).repeat(column=['Height', 'Diameter']).properties(title=Boxplot_Titles)

Neighbour_BoxPlot

Looking at the Height graph, the median of all neighborhoods seem to hover around 2 to 3, and most, except for Sunset, share a minimum of 1; this distribution reflects the reality more closely as the entries listed were of ranges, instead of definitive values.
Shifting our attention to the other Diameter graph, the median of the majority of neighbourhoods seems to hover around 10, but the minimum value looks to vary more drastically, ranging from 0 to 3. The outliers for the diameter graph are substantially more in quantity and with greater variation!

Question 03: What are the numbers of trees of all genera in each neighborhood?

To adequately plot all genera in all neighbourhoods, it is best to put together a scatterplot with 'Neighbourhood' on the X-axis and 'Genus' on the Y-axis; such a graph will showcase the total count of trees. The 'count()' function will be fitted to the color & size channel to more clearly enhance the quantity difference visually. Additionally, tooltip will be included, which will illustrate the exact count of records, and a dropdown menu will be featured to give the readers/audience the ability to filter which exact 'Neighbourhood' to focus on.

# Scatterplot title and subtitle
Scatterplot_Title = alt.TitleParams('Fig 04: "Acer" and "Prunus" are the most common genera in most neighbourhoods', 
                                   subtitle='Scatterplot of all genera in all neighbourhoods',
                                   anchor='middle')

# Neighbourhood slider for better clarity
Neighbourhood_Unique = sorted(trees_df['Neighbourhood'].unique())
Neighbourhood_Dropdown = alt.binding_select(name='Figure 04: Neighbourhood selection ', options=Neighbourhood_Unique)
Neighbourhood_Select = alt.selection_single(fields=['Neighbourhood'], bind=Neighbourhood_Dropdown)

# Figure 04
Genus_ScatterPlot = alt.Chart(trees_df).mark_circle().encode(
    alt.X('Genus'), 
    alt.Y('Neighbourhood'), 
    alt.Color('count()', title='Amount of trees'), 
    alt.Size('count()'),
    opacity=alt.condition(Neighbourhood_Select, alt.value(0.8), alt.value(0.05)),
    tooltip=['count()']).properties(title=Scatterplot_Title).add_selection(Neighbourhood_Select)

Genus_ScatterPlot

Looking at the output of Figure 04, the genera 'Acer' and 'Prunus' are the most common in most neighbourhoods. Several genera, such as 'Koelreuteria' and 'Ilex', are none-existing in most neighbourhoods (and overall). 'Fraxinus', 'Carpinus', and a few others seem to sit more comfortably in the middle, but the difference between them and the two reigning genera is still quite significant.

Question 04: What are the diameter and height range for all trees in all neighbourhoods and of all genera?

Similar to Question 03, we will also be plotting a scatterplot, except there will be more information and columns involved. 'Diameter' will be set to the X-axis and 'Neighbourhood' will be set to the Y-axis. The graph will also include a slider for the 'Height' column, with additional parameters of set=1 & max=9 to contain the slider values properly, and tooltip to communicate the exact values of 'Height', 'Diameter', 'count()', and 'genus'.

# Scatterplot title and subtitle
Scatterplot_Title_Fig05 = alt.TitleParams('Fig 05: Most trees lie within the diameter range of 2 to 36', 
                                   subtitle='Over a dozen outliers are over 45 in diameter',
                                   anchor='middle')

# Slider binding and selection for the 'Height' column
Height_Slider = alt.binding_range(name='Figure 05: Height range selection ', step=1, max=9)
Height_Select = alt.selection_single(
    fields=['Height'], bind=Height_Slider)

# Figure 06
All_ScatterPlot = alt.Chart(trees_df).mark_circle(size=30).encode(
    alt.X('Diameter:Q', scale=alt.Scale(domain=[0, 75])), 
    alt.Y('Neighbourhood:N'),
    opacity=alt.condition(Height_Select, alt.value(0.8), alt.value(0.01)),
    tooltip=['Height', 'Diameter', 'count()', 'Genus']
    ).add_selection(Height_Select).properties(title=Scatterplot_Title_Fig05, width=1000).interactive()

All_ScatterPlot

The majority of trees seem to lie within the diameter range of 2 to 36; going above that range, the amount of trees fall off drastically. There are also quite a few outliers, especially the ones over 45 in diameter (roughly a dozen). By using the 'Height' slider, it is evident that the higher the value (the taller the trees), the lesser the quantity. Once the slider goes above 6, the number of circles diminishes quite substanitally. Vancouver does not seem to have a lot of tall trees.

Discussion

Vancouver has always been known for its outdoor lifestyle and nature-rich environment, so certain questions and answers that I found were not all that surprising; however, some did manage to catch me off-guard. Let's break it down from the beginning!

In Figure 01, we can see that Renfrew-Collingwood takes the crown for having the most amount of trees planted in total. Nore than half of all neighbourhoods seem to be situated at around 150 to 300, while Strathcona unfortunately having the least amount of trees with only 75. In Figure 02, it is not a surprise to learn that growing on and off the curb makes a difference, but what is surprising for me is seeing the difference and how drastic it is! Dunbar-Southlands takes home the title of having the most amount of trees planted off the curb at 63, but the figure dwarfs significantly when compared to how many trees the nighbourhood has growing on the curb of 250; there are four times more trees planted on the curb than off the curb! All other neighbourhoods share similar results, with the difference ratio being even more magnificent!

Moving on to Figure 03, the boxplot conveys a lot of information to us, both for 'Height' and 'Diameter' distributions. Let's first look at the 'Height' boxplot. The median value for height range is around 2 to 3, while the third quartile is more commonly situated at 4. Only five neighbourhoods don't have a an outlier, but two of them have a maximum of 9, which is also the max height range from the data set. Shifting our focus now to the 'Diameter', the variation of each category of information bewteen each neighbourhood stats increases significantly. The median value seems to hover around 10, with greater variation than 'Height' boxplot. All neighbourhoods have a minimum of lower than 10 and various different maximum value. Downtown seems to have the smallest degree of difference, as their first quartile being 4 and third quartile sitting exactly at 10; such a statistic makes perfect sense once you consider how limited and crowded the downtown area is, thus not allowing many trees to growing bigger and wider.

What astonishes me about Figure 04 is how dominant certain genera are when compared to others. 'Acer' and 'Prunus' completely overshadow all other genera in all neighbourhoods. Kensington-Cedar Cottage has the most amount of 'Acer', 90, while Victoria-Fraser possesses the largest quantity of 'Prunus' at 94. About a third of all genera have a very limited range of entries in most neighbourhoods, such as 'Chitalpa' and 'Nyssa' (to name a few). Not being a botanist myself, it is difficult to ascertain how certain genera gain their dominance in Vancouver, but putting aside the potential reasons why, the stats paint a stark picture of a winner-take-all situation and a highly imbalanced distribution of genera.

Lastly, looking at Figure 05, perhaps the most complex graph of this report, we can see most trees fall between the diameter range of 2 to 36. As we shift the slider more to the right, increasing the height, the diameter value grows and the thicker most trees become; putting that into the context of our reality, a tall tree is more likely going to require a thicker trunk for it to sustain its height. While not every tall tree has a wide diameter, but the overall distribution based on the visual changes from the slider selection confirms that assumption. Interestingly, the two trees with the widest/thickest diameter of 71 only have the height range of 5 (but then again, that's why they are outliers!).

Originally, I did intend to explore more of the evolution of tree diameters and height ranges over time, but due to the amount of null values present in its temporal category, I had to shift focus onto examining all neighbourhoods instead; questions, such as has the diameter of trees gotten thicker or the height of trees gotten taller, were scraped as a result. Another gripe I have against this data set is its definition of 'Height range'. Without exact values being inputted, we lose a lot of details in our analysis and our ability to create more sophisticated graphs become limited.

Despite the limitation of the data set, the report and dashboard have been quite satisfying for me to put together, and its results have been welcoming as well (both for confirming my own pre-conceived notions as well as surprising me in some aspects). I hope you have enjoyed reading this analysis as much as I have compiling it!

Dashboard

The following dashboard is comprised of all the graphs that I have made in this report. Figure 01 and 02 will be concated horizontally, as they are designed to be viewed with and against each other for better comparison, while 03, 04 and 05 will be stacked beneath them vertically. Figure 05 is designed to be the ultimate combination of all the graphs that came before it; therefore, it is situated at the very bottom of the report. One improvement that I would've liked to make, should I have more time and technical know-hows to achieve it, is to attach the Figure 04 dropdown menu and Figure 05 slider beneath their respective graphs. Having the two selection tools clustered at the bottom and out-of-order just seem messy to me. But the dashboard, as it stands currently, serves the purpose of answering all of my proposed questions visually and allows the reader/audience to interact with it with ease and clarity, so it's good enough!

(Neighbour_Histogram & Neighbour_BoxPlot) & (Genus_ScatterPlot.encode(tooltip=['count()']).properties(width=1000) & All_ScatterPlot)

Reference

1. Introduction to Data Visualization. University of British Columbia. https://canvas.ubc.ca/course/140055
2. Vancouver Street Trees. City of Vancouver. https://opendata.vancouver.ca/explore/dataset/street-trees/information/?disjunctive.species_name&disjunctive.common_name&disjunctive.height_range_id&disjunctive.on_street&disjunctive.neighbourhood_name